Federated Learning (FL) was proposed in [1]}, [2]} as a new paradigm for tackling large-scale distributed machine learning problems with distinct features compared to the data-center setting:
(i) massive client population; (ii) limited communication resources; (iii) low device participation rate and unreliable connections; (iv) stringent privacy considerations. A canonical setting of FL is shown in Fig. REF , where a server coordinates with clients to compute a global ML model.
<FIGURE>